An Accurate and Automated Method for Adenoma Detection Rate and Report Card Generation Utilizing Common Electronic Health Records

Abstract

Goals: To develop an automated method for Adenoma Detection Rate (ADR) calculation and report card generation using common electronic health records (EHRs). Background: ADR is the most widely accepted colonoscopy quality indicator and is inversely associated with interval colorectal cancer incidence and mortality. However, ADR is difficult to efficiently measure and disseminate, due to need for data integration from distinct electronic databases. Methods: We migrated data from an endoscopy reporting software (Endosoft) to Epic Reporting Servers where it was combined with anatomic pathology data (Beaker Lab Information System, EPIC Systems). A natural language processing expression was developed to search Beaker pathology reports for accurate identification of adenomatous polyps. A blinded physician manually validated a final cohort of 200 random procedures. ADR report cards were automatically generated utilizing the Crystal Reports feature within EPIC. Results: Validation of the natural language processing algorithm for ADR showed a sensitivity, specificity, and accuracy of 100%. ADR was automatically calculated for 12 endoscopists over a calendar year. Two thousand two hundred seventy-six screening colonoscopies were performed with 775 procedures having a least one adenoma detected, for a total ADR of 34%. Report cards were successfully generated within the EPIC EHR and distributed to endoscopists by secure e-mail. Conclusion: We describe an accurate, automated and scalable process for ADR calculation and reporting utilizing commonly adopted EHRs and data integration methods. By integrating the process of ADR collection and streamlining dissemination of reports, this methodology is poised to enhance colonoscopy quality care across health care networks that use it.